--- library_name: transformers language: - en - fr - it - pt - hi - es - th - de base_model: - meta-llama/Llama-3.1-70B-Instruct tags: - facebook - meta - pytorch - llama - llama-3 - int4 - quantized license: llama3.3 ---

Llama-3.3-70B-Instruct-quantized.w4a16 Model Icon

Validated Badge ## Model Overview - **Model Architecture:** Meta-Llama-3.1 - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** INT4 - **Intended Use Cases:** Intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. The Llama 3.3 model also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 3.3 Community License allows for these use cases. - **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.3 Community License. Use in languages beyond English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. - **Release Date:** 12/11/2024 - **Version:** 1.0 - **License(s):** llama3.3 - **Model Developers:** Red Hat (Neural Magic) ### Model Optimizations This model was obtained by quantizing the weights of [Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) to INT4 data type. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%. Only the weights of the linear operators within transformers blocks are quantized. Weights are quantized using a symmetric per-group scheme, with group size 128. The [GPTQ](https://arxiv.org/abs/2210.17323) algorithm is applied for quantization, as implemented in the [llm-compressor](https://github.com/vllm-project/llm-compressor) library. ## Deployment This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below. ```python from vllm import LLM, SamplingParams from transformers import AutoTokenizer model_id = "RedHatAI/Llama-3.3-70B-Instruct-quantized.w4a16" number_gpus = 1 sampling_params = SamplingParams(temperature=0.6, top_p=0.9, max_tokens=256) tokenizer = AutoTokenizer.from_pretrained(model_id) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] prompts = tokenizer.apply_chat_template(messages, tokenize=False) llm = LLM(model=model_id, tensor_parallel_size=number_gpus) outputs = llm.generate(prompts, sampling_params) generated_text = outputs[0].outputs[0].text print(generated_text) ``` vLLM aslo supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
Deploy on Red Hat AI Inference Server ```bash $ podman run --rm -it --device nvidia.com/gpu=all -p 8000:8000 \ --ipc=host \ --env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \ --env "HF_HUB_OFFLINE=0" -v ~/.cache/vllm:/home/vllm/.cache \ --name=vllm \ registry.access.redhat.com/rhaiis/rh-vllm-cuda \ vllm serve \ --tensor-parallel-size 8 \ --max-model-len 32768 \ --enforce-eager --model RedHatAI/Llama-3.3-70B-Instruct-quantized.w4a16 ``` ​​See [Red Hat AI Inference Server documentation](https://docs.redhat.com/en/documentation/red_hat_ai_inference_server/) for more details.
Deploy on Red Hat Enterprise Linux AI ```bash # Download model from Red Hat Registry via docker # Note: This downloads the model to ~/.cache/instructlab/models unless --model-dir is specified. ilab model download --repository docker://registry.redhat.io/rhelai1/llama-3-3-70b-instruct-quantized-w4a16:1.5 ``` ```bash # Serve model via ilab ilab model serve --model-path ~/.cache/instructlab/models/llama-3-3-70b-instruct-quantized-w4a16 # Chat with model ilab model chat --model ~/.cache/instructlab/models/llama-3-3-70b-instruct-quantized-w4a16 ``` See [Red Hat Enterprise Linux AI documentation](https://docs.redhat.com/en/documentation/red_hat_enterprise_linux_ai/1.4) for more details.
Deploy on Red Hat Openshift AI ```python # Setting up vllm server with ServingRuntime # Save as: vllm-servingruntime.yaml apiVersion: serving.kserve.io/v1alpha1 kind: ServingRuntime metadata: name: vllm-cuda-runtime # OPTIONAL CHANGE: set a unique name annotations: openshift.io/display-name: vLLM NVIDIA GPU ServingRuntime for KServe opendatahub.io/recommended-accelerators: '["nvidia.com/gpu"]' labels: opendatahub.io/dashboard: 'true' spec: annotations: prometheus.io/port: '8080' prometheus.io/path: '/metrics' multiModel: false supportedModelFormats: - autoSelect: true name: vLLM containers: - name: kserve-container image: quay.io/modh/vllm:rhoai-2.20-cuda # CHANGE if needed. If AMD: quay.io/modh/vllm:rhoai-2.20-rocm command: - python - -m - vllm.entrypoints.openai.api_server args: - "--port=8080" - "--model=/mnt/models" - "--served-model-name={{.Name}}" env: - name: HF_HOME value: /tmp/hf_home ports: - containerPort: 8080 protocol: TCP ``` ```python # Attach model to vllm server. This is an NVIDIA template # Save as: inferenceservice.yaml apiVersion: serving.kserve.io/v1beta1 kind: InferenceService metadata: annotations: openshift.io/display-name: llama-3-3-70b-instruct-quantized-w4a16 # OPTIONAL CHANGE serving.kserve.io/deploymentMode: RawDeployment name: llama-3-3-70b-instruct-quantized-w4a16 # specify model name. This value will be used to invoke the model in the payload labels: opendatahub.io/dashboard: 'true' spec: predictor: maxReplicas: 1 minReplicas: 1 model: modelFormat: name: vLLM name: '' resources: limits: cpu: '2' # this is model specific memory: 8Gi # this is model specific nvidia.com/gpu: '1' # this is accelerator specific requests: # same comment for this block cpu: '1' memory: 4Gi nvidia.com/gpu: '1' runtime: vllm-cuda-runtime # must match the ServingRuntime name above storageUri: oci://registry.redhat.io/rhelai1/modelcar-llama-3-3-70b-instruct-quantized-w4a16:1.5 tolerations: - effect: NoSchedule key: nvidia.com/gpu operator: Exists ``` ```bash # make sure first to be in the project where you want to deploy the model # oc project # apply both resources to run model # Apply the ServingRuntime oc apply -f vllm-servingruntime.yaml # Apply the InferenceService oc apply -f qwen-inferenceservice.yaml ``` ```python # Replace and below: # - Run `oc get inferenceservice` to find your URL if unsure. # Call the server using curl: curl https://-predictor-default./v1/chat/completions -H "Content-Type: application/json" \ -d '{ "model": "llama-3-3-70b-instruct-quantized-w4a16", "stream": true, "stream_options": { "include_usage": true }, "max_tokens": 1, "messages": [ { "role": "user", "content": "How can a bee fly when its wings are so small?" } ] }' ``` See [Red Hat Openshift AI documentation](https://docs.redhat.com/en/documentation/red_hat_openshift_ai/2025) for more details.
## Creation
Creation details This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below. ```python from transformers import AutoModelForCausalLM, AutoTokenizer from llmcompressor.modifiers.quantization import GPTQModifier from llmcompressor.transformers import oneshot from datasets import load_dataset # Load model model_stub = "meta-llama/Llama-3.3-70B-Instruct" model_name = model_stub.split("/")[-1] num_samples = 1024 max_seq_len = 8192 tokenizer = AutoTokenizer.from_pretrained(model_stub) model = AutoModelForCausalLM.from_pretrained( model_stub, device_map="auto", torch_dtype="auto", ) def preprocess_fn(example): return {"text": tokenizer.apply_chat_template(example["messages"], add_generation_prompt=False, tokenize=False)} ds = load_dataset("neuralmagic/LLM_compression_calibration", split="train") ds = ds.map(preprocess_fn) # Configure the quantization algorithm and scheme recipe = GPTQModifier( targets="Linear", scheme="W4A16", ignore=["lm_head"], sequential_targets=["LlamaDecoderLayer"], dampening_frac=0.01, ) # Apply quantization oneshot( model=model, dataset=ds, recipe=recipe, max_seq_length=max_seq_len, num_calibration_samples=num_samples, ) # Save to disk in compressed-tensors format save_path = model_name + "-quantized.w4a16" model.save_pretrained(save_path) tokenizer.save_pretrained(save_path) print(f"Model and tokenizer saved to: {save_path}") ```
## Evaluation This model was evaluated on the well-known OpenLLM v1, HumanEval, and HumanEval+ benchmarks. In all cases, model outputs were generated with the [vLLM](https://docs.vllm.ai/en/stable/) engine. OpenLLM v1 evaluations were conducted using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) and the prompting style of [Meta-Llama-3.1-Instruct-evals](https://huggingface.co/datasets/meta-llama/Meta-Llama-3.1-8B-Instruct-evals) when available. HumanEval and HumanEval+ evaluations were conducted using Neural Magic's fork of the [EvalPlus](https://github.com/neuralmagic/evalplus) repository.
Evaluation details **MMLU** ``` lm_eval \ --model vllm \ --model_args pretrained="RedHatAI/Llama-3.3-70B-Instruct-quantized.w4a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \ --tasks mmlu_llama \ --fewshot_as_multiturn \ --apply_chat_template \ --num_fewshot 5 \ --batch_size auto ``` **MMLU-CoT** ``` lm_eval \ --model vllm \ --model_args pretrained="RedHatAI/Llama-3.3-70B-Instruct-quantized.w4a16",dtype=auto,max_model_len=4064,max_gen_toks=1024,tensor_parallel_size=1 \ --tasks mmlu_cot_llama \ --apply_chat_template \ --num_fewshot 0 \ --batch_size auto ``` **ARC-Challenge** ``` lm_eval \ --model vllm \ --model_args pretrained="RedHatAI/Llama-3.3-70B-Instruct-quantized.w4a16",dtype=auto,max_model_len=3940,max_gen_toks=100,tensor_parallel_size=1 \ --tasks arc_challenge_llama \ --apply_chat_template \ --num_fewshot 0 \ --batch_size auto ``` **GSM-8K** ``` lm_eval \ --model vllm \ --model_args pretrained="RedHatAI/Llama-3.3-70B-Instruct-quantized.w4a16",dtype=auto,max_model_len=4096,max_gen_toks=1024,tensor_parallel_size=1 \ --tasks gsm8k_llama \ --fewshot_as_multiturn \ --apply_chat_template \ --num_fewshot 8 \ --batch_size auto ``` **Hellaswag** ``` lm_eval \ --model vllm \ --model_args pretrained="RedHatAI/Llama-3.3-70B-Instruct-quantized.w4a16",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \ --tasks hellaswag \ --num_fewshot 10 \ --batch_size auto ``` **Winogrande** ``` lm_eval \ --model vllm \ --model_args pretrained="RedHatAI/Llama-3.3-70B-Instruct-quantized.w4a16",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \ --tasks winogrande \ --num_fewshot 5 \ --batch_size auto ``` **TruthfulQA** ``` lm_eval \ --model vllm \ --model_args pretrained="RedHatAI/Llama-3.3-70B-Instruct-quantized.w4a16",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \ --tasks truthfulqa \ --num_fewshot 0 \ --batch_size auto ``` **HumanEval and HumanEval+** *Generation* ``` python3 codegen/generate.py \ --model RedHatAI/Llama-3.3-70B-Instruct-quantized.w4a16 \ --bs 16 \ --temperature 0.2 \ --n_samples 50 \ --root "." \ --dataset humaneval ``` *Sanitization* ``` python3 evalplus/sanitize.py \ humaneval/RedHatAI--Llama-3.3-70B-Instruct-quantized.w4a16_vllm_temp_0.2 ``` *Evaluation* ``` evalplus.evaluate \ --dataset humaneval \ --samples humaneval/RedHatAI--Llama-3.3-70B-Instruct-quantized.w4a16_vllm_temp_0.2-sanitized ```
### Accuracy
Category Benchmark Llama-3.3-70B-Instruct Llama-3.3-70B-Instruct-quantized.w4a16
(this model) 		Recovery
OpenLLM v1 MMLU (5-shot) 81.60 80.62 98.8%
MMLU (CoT, 0-shot) 86.58 85.81 99.1%
ARC Challenge (0-shot) 49.23 49.49 100.5%
GSM-8K (CoT, 8-shot, strict-match) 94.16 94.47 100.3%
Hellaswag (10-shot) 86.49 85.97 99.4%
Winogrande (5-shot) 84.77 %
TruthfulQA (0-shot, mc2) 62.75 61.66 98.3%
Average 77.94 77.49 98.3%
Coding HumanEval pass@1 83.20 83.40 100.2%
HumanEval+ pass@1 78.40 78.60 100.3%